Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 1

Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by the National Aeronautics and Space Administration and cosponsored by the University of Houston, Clear Lake. The workshop was held June 1-3, 1992 at the Lyndon B. Johnson Space Center in Houston, Texas. During the three days approximately 50 papers were presented. Technical topics addressed included adaptive systems; learning algorithms; network architectures; vision; robotics; neurobiological connections; speech recognition and synthesis; fuzzy set theory and application, control, and dynamics processing; space applications; fuzzy logic and neural network computers; approximate reasoning; and multiobject decision making

Combined AI Capabilities for Enhancing Maritime Safety in a Common Information Sharing Environment

The complexity of maritime traffic operations indicates an unprecedented necessity for joint introduction and exploitation of artificial intelligence (AI) technologies, that take advantage of the vast amount of vessels’ data, offered by disparate surveillance systems to face challenges at sea. This paper reviews the recent Big Data and AI technology implementations for enhancing the maritime safety level in the common information sharing environment (CISE) of the maritime agencies, including vessel behavior and anomaly monitoring, and ship collision risk assessment. Specifically, the trajectory fusion implemented with InSyTo module for soft information fusion and management toolbox, and the Early Notification module for Vessel Collision are presented within EFFECTOR Project. The focus is to elaborate technical architecture features of these modules and combined AI capabilities for achieving the desired interoperability and complementarity between maritime systems, aiming to provide better decision support and proper information to be distributed among CISE maritime safety stakeholders

Vision Goes Symbolic Without Loss of Information Within the Preattentive Vision Phase: The Need to Shift the Learning Paradigm from Machine-Learning (from Examples) to Machine-Teaching (by Rules) at the First Stage of a Two-Stage Hybrid Remote... Part I

Life sciences: general issue

A novel bottleneck residual and self-attention fusion-assisted architecture for land use recognition in remote sensing images

The massive yearly population growth is causing hazards to spread swiftly around the world and have a detrimental impact on both human life and the world economy. By ensuring early prediction accuracy, remote sensing enters the scene to safeguard the globe against weather-related threats and natural disasters. Convolutional neural networks, which are a reflection of deep learning, have been used more recently to reliably identify land use in remote sensing images. This work proposes a novel bottleneck residual and self-attention fusion-assisted architecture for land use recognition from remote sensing images. First, we proposed using the fast neural approach to generate cloud-effect satellite images. In neural style, we proposed a 5-layered residual block CNN to estimate the loss of neural-style images. After that, we proposed two novel architectures, named 3-layered bottleneck CNN architecture and 3-layered bottleneck self-attention CNN architecture, for the classification of land use images. Training has been conducted on both proposed and original neural-style generated datasets for both architectures. Subsequently, features are extracted from the deep layers and merged employing an innovative serial approach based on weighted entropy. By removing redundant and superfluous data, a novel Chimp Optimization technique is applied to the fused features in order to further refine them. In conclusion, selected features are classified using the help of neural network classifiers. The experimental procedure yielded respective accuracy rates of 99.0% and 99.4% when applied to both datasets. When evaluated in comparison to state-of-the-art (SOTA) methods, the outcomes generated by the proposed framework demonstrated enhanced precision and accuracy

Internet of Vehicles: Motivation, Layered Architecture, Network Model, Challenges, and Future Aspects

© 2013 IEEE. Internet of Things is smartly changing various existing research areas into new themes, including smart health, smart home, smart industry, and smart transport. Relying on the basis of 'smart transport,' Internet of Vehicles (IoV) is evolving as a new theme of research and development from vehicular ad hoc networks (VANETs). This paper presents a comprehensive framework of IoV with emphasis on layered architecture, protocol stack, network model, challenges, and future aspects. Specifically, following the background on the evolution of VANETs and motivation on IoV an overview of IoV is presented as the heterogeneous vehicular networks. The IoV includes five types of vehicular communications, namely, vehicle-to-vehicle, vehicle-to-roadside, vehicle-to-infrastructure of cellular networks, vehicle-to-personal devices, and vehicle-to-sensors. A five layered architecture of IoV is proposed considering functionalities and representations of each layer. A protocol stack for the layered architecture is structured considering management, operational, and security planes. A network model of IoV is proposed based on the three network elements, including cloud, connection, and client. The benefits of the design and development of IoV are highlighted by performing a qualitative comparison between IoV and VANETs. Finally, the challenges ahead for realizing IoV are discussed and future aspects of IoV are envisioned

Expert Systems and Artificial Neural Networks for Spatial Analysis and Modelling: Essential Components for Knowledge-Based Geographical Information Systems

Series: Discussion Papers of the Institute for Economic Geography and GIScienc